splsda: Fit SPLSDA classification models

In spls: Sparse Partial Least Squares (SPLS) Regression and Classification

Description

Fit a SPLSDA classification model.

Usage

splsda( x, y, K, eta, kappa=0.5,
    classifier=c('lda','logistic'), scale.x=TRUE, ... )

Arguments

x	Matrix of predictors.
y	Vector of class indices.
K	Number of hidden components.
eta	Thresholding parameter. eta should be between 0 and 1.
kappa	Parameter to control the effect of the concavity of the objective function and the closeness of original and surrogate direction vectors. kappa is relevant only for multicategory classification. kappa should be between 0 and 0.5. Default is 0.5.
classifier	Classifier used in the second step of SPLSDA. Alternatives are "logistic" or "lda". Default is "lda".
scale.x	Scale predictors by dividing each predictor variable by its sample standard deviation?
...	Other parameters to be passed through to spls.

Details

The SPLSDA method is described in detail in Chung and Keles (2010). SPLSDA provides a two-stage approach for PLS-based classification with variable selection, by directly imposing sparsity on the dimension reduction step of PLS using sparse partial least squares (SPLS) proposed in Chun and Keles (2010). y is assumed to have numerical values, 0, 1, ..., G, where G is the number of classes subtracted by one. The option classifier refers to the classifier used in the second step of SPLSDA and splsda utilizes algorithms offered by MASS and nnet packages for this purpose. If classifier="logistic", then either logistic regression or multinomial regression is used. Linear discriminant analysis (LDA) is used if classifier="lda". splsda also utilizes algorithms offered by the pls package for fitting spls. The user should install pls, MASS and nnet packages before using splsda functions.

Value

A splsda object is returned. print, predict, coef methods use this object.

Author(s)

Dongjun Chung and Sunduz Keles.

References

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

See Also

print.splsda, predict.splsda, and coef.splsda.

Examples

data(prostate)
# SPLSDA with eta=0.8 & 3 hidden components
f <- splsda( prostate$x, prostate$y, K=3, eta=0.8, scale.x=FALSE )
print(f)
# Print out coefficients
coef.f <- coef(f)
coef.f[ coef.f!=0, ]

Example output

Sparse Partial Least Squares (SPLS) Regression and
Classification (version 2.2-2)


Sparse Partial Least Squares Discriminant Analysis
----
Parameters: eta = 0.8, K = 3
Classifier: Linear Discriminant Analysis (LDA)

SPLSDA chose 44 variables among 6033 variables

Selected variables: 
54	105	118	126	127	
292	306	308	526	535	
665	1455	1839	2425	2619	
3006	3032	3118	3183	3300	
3423	3587	3665	3743	3826	
3858	3950	4091	4155	4288	
4353	4448	4498	4701	5016	
5214	5248	5249	5343	5344	
5742	5784	5808	5983	
         x54         x105         x118         x126         x127         x292 
-0.079858427  0.062790549  0.200050675  0.151102721  0.125336124  0.097054753 
        x306         x308         x526         x535         x665        x1455 
 0.072054245 -0.035031567  0.011647612 -0.033456449 -0.003465278  0.111368189 
       x1839        x2425        x2619        x3006        x3032        x3118 
 0.344341295  0.222652673  0.502701945  0.118372469  0.037222124  0.209313921 
       x3183        x3300        x3423        x3587        x3665        x3743 
 0.070443118 -0.122593897  0.402374565 -0.049870115  0.076675783 -0.057151555 
       x3826        x3858        x3950        x4091        x4155        x4288 
-0.166120994 -0.008313119 -0.002198880  0.024707507  0.213824118 -0.379608007 
       x4353        x4448        x4498        x4701        x5016        x5214 
 0.034728924  0.234539768  0.072599251 -0.406717619 -0.417611040 -0.144379126 
       x5248        x5249        x5343        x5344        x5742        x5784 
 0.018682757  0.088453884  0.064227582 -0.314783831 -0.137700748 -0.137121968 
       x5808        x5983 
 0.130724156 -0.287282180 

spls documentation built on May 6, 2019, 1:09 a.m.